DataNova 0.1.3

A friendly toolkit for quick exploratory data analysis (EDA)

🌌 DataNova

DataNova — a toolkit for data exploration in Python with a few lines of code!

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🚀 Features

• Instant profiling: Summarize your data with profile(df)
• Bar Graph: Shows the top 5 most common values bar(df, 'Column_Name')
• Histogram: Plots the distribution of numerical data hist(df, 'Column_Name')
• Exploratory Data Analysis: EDA(df)
• Simple Linear Regression: lm(df, x_var, y_var)

what's next?

• Logistic Regression (coming soon!)

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🧭 Examples

Examples below use a dataset provided in the package called WINE_DF

from datanova import *
profile( WINE_DF )

• Most of our data is not blank, expect for 'price' (only 5% blank).

	Variable Name	Variable Type	Missing Count	% Blank	Unique Values	Most Frequent Value	Mean	Standard Deviation	Min	25%	Median	75%	Max
0	country	object	0	0	24	US
1	province	object	0	0	120	California
2	points	int64	0	0	13	90	89.55	2.32	84.0	88.0	90.0	91.0	96.0
3	price	float64	88	5	110	20.0	38.71	29.39	7.0	20.0	30.0	48.0	500.0
4	variety	object	0	0	161	Pinot Noir

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bar( WINE_DF , 'province', top_n=7)

• California accounts for 32% of total wine sales, and the top five regions collectively contribute over half of all sales.

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hist( WINE_DF , 'price' , xlim = [0,105], n_bins = 25)

• On average, a bottle of wine costs $38. The price ranges from: $20-$48

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eda( WINE_DF )

• Creates bar graphs and histograms for all columns in the dataset.

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Regression Modeling

figure, model = lm(WINE_DF, 'points' , 'price' ,  ylimit = [0,150] ,
                   xtitle = 'Points' , ytitle = 'Price ($)' , 
                   show_summary=False , alpha = 0.8 ) 

display( figure )  

• Creates a simple linear regression
• As the quality of wine increases (points), the price also increases.

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🛠️ Installation

pip install DataNova